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Using logistic regression to estimate the influence of accident factors on accident severity

机译：使用逻辑回归估计事故因素对事故严重性的影响

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摘要

Logistic regression was applied to accident-related data collected from traffic police records in order to examine the contribution of several variables to accident severity. A total of 560 subjects involved in serious accidents were sampled. Accident severity (the dependent variable) in this study is a dichotomous variable with two categories, fatal and non-fatal. Therefore, each of the subjects sampled was classified as being in either a fatal or non-fatal accident. Because of the binary nature of this dependent variable, a logistic regression approach was found suitable. Of nine independent variables obtained from police accident reports, two were found most significantly associated with accident severity, namely, location and cause of accident. A statistical interpretation is given of the model-developed estimates in terms of the odds ratio concept. The findings show that logistic regression as used in this research is a promising tool in providing meaningful interpretations that can be used for future safety improvements in Riyadh.
机译：Logistic回归应用于从交通警察记录中收集的与事故相关的数据，以检查多个变量对事故严重性的影响。总共对560名严重事故受试者进行了采样。这项研究中的事故严重性（因变量）是两类变量，分为致命和非致命两类。因此，每个样本被分类为致命或非致命事故。由于该因变量的二进制性质，发现逻辑回归方法是合适的。从警察事故报告中获得的9个独立变量中，发现有2个与事故严重程度最相关，即位置和事故原因。根据比值比概念对模型开发的估计值进行统计解释。研究结果表明，本研究中使用的逻辑回归是一种有前途的工具，可提供有意义的解释，可用于利雅得未来的安全改进。

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